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ICES
2003
Springer
125views Hardware» more  ICES 2003»
15 years 6 months ago
Evolving Reinforcement Learning-Like Abilities for Robots
Abstract. In [8] Yamauchi and Beer explored the abilities of continuous time recurrent neural networks (CTRNNs) to display reinforcementlearning like abilities. The investigated ta...
Jesper Blynel
AAAI
2008
15 years 3 months ago
Potential-based Shaping in Model-based Reinforcement Learning
Potential-based shaping was designed as a way of introducing background knowledge into model-free reinforcement-learning algorithms. By identifying states that are likely to have ...
John Asmuth, Michael L. Littman, Robert Zinkov
NIPS
2007
15 years 2 months ago
Online Linear Regression and Its Application to Model-Based Reinforcement Learning
We provide a provably efficient algorithm for learning Markov Decision Processes (MDPs) with continuous state and action spaces in the online setting. Specifically, we take a mo...
Alexander L. Strehl, Michael L. Littman
EUSFLAT
2001
144views Fuzzy Logic» more  EUSFLAT 2001»
15 years 2 months ago
Adaptive torque control using a connectionist reinforcement learning agent
The correction of angular misalignment between mating components is a fundamental requirement for their successful assembly. In this paper we present how a learning agent based on...
Lorenzo Brignone, Martin Howarth, S. Sivayoganatha...
FLAIRS
2004
15 years 2 months ago
State Space Reduction For Hierarchical Reinforcement Learning
er provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, ...
Mehran Asadi, Manfred Huber